{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:23:46Z","timestamp":1787027026283,"version":"build-2736575974"},"reference-count":14,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T00:00:00Z","timestamp":1651708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:p>The objective of this research is to explore the value of whole-thyroid CT-based radiomics in predicting benign (noncancerous) and malignant thyroid nodules. The imaging and clinical data of 161 patients with thyroid nodules that were confirmed by pathology were retrospectively analyzed. The entire thyroid regions of interest (ROIs) were manually sketched for all 161 cases. After extracting CT radiomic features, the patients were divided into a training group (128 cases) and a test group (33 cases) according to the 4:1 ratio with stratified random sampling (fivefold cross validation). All the data were normalized by the maximum absolute value and screened using selection operator regression analysis and K best. The data generation model was trained by logistic regression. The effectiveness of the model in differentiating between benign and malignant thyroid nodules was validated by a receiver operating characteristic (ROC) curve. After data grouping, eigenvalue screening, and data training, the logistic regression model with the maximum absolute value normalized was constructed. For the training group, the area under the ROC curve (AUC) was 94.4% (95% confidence interval: 0.941\u20130.977); the sensitivity and specificity were 89.7% and 86.7%, respectively; and the diagnostic accuracy was 87.6%. For the test group, the AUC was 94.2% (95% confidence interval: 0.881\u20130.999); the sensitivity and specificity were 89.4% and 86.8%, respectively; and the diagnostic accuracy was 87.6%. The CT radiomic model of the entire thyroid gland is highly efficient in differentiating between benign and malignant thyroid nodules.<\/jats:p>","DOI":"10.3389\/fonc.2022.828259","type":"journal-article","created":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T01:21:07Z","timestamp":1651713667000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Value of Whole-Thyroid CT-Based Radiomics in Predicting Benign and Malignant Thyroid Nodules"],"prefix":"10.3389","volume":"12","author":[{"given":"Han","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ximing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaoqun","family":"Guan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ru","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aie","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingwei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,5,5]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2018.0898","article-title":"The Diagnosis and Management of Thyroid Nodules: A Review","volume":"319","author":"Durante","year":"2018","journal-title":"JAMA"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: Images Are More Than Pictures, They Are Data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"B3","doi-asserted-by":"publisher","first-page":"185","DOI":"10.3760\/cma.j.cn131148-20210205-00092","article-title":"2020 Chinese Guidlines Fot Ultrosound Malignancy Risk Stratification of Thyroid Nodules : The C-TIRADS","volume":"30","year":"2021","journal-title":"Chin J Ultrasonography"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2015.65.9128","article-title":"Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer","volume":"34","author":"Huang","year":"2016","journal-title":"J Clin Oncol"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020192431","article-title":"Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study","volume":"297","author":"Wu","year":"2020","journal-title":"Radiology"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1007\/s10549-018-4675-4","article-title":"Rapid Review: Radiomics and Breast Cancer","volume":"169","author":"Valdora","year":"2018","journal-title":"Breast Cancer Res Treat"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.A6505","article-title":"Radiomics Study of Thyroid Ultrasound for Predicting BRAF Mutation in Papillary Thyroid Carcinoma: Preliminary Results","volume":"41","author":"Kwon","year":"2020","journal-title":"AJNR Am J Neuroradiol"},{"key":"B8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ejrad.2017.12.004","article-title":"Texture Analysis and Machine Learning to Characterize Suspected Thyroid Nodules and Differentiated Thyroid Cancer: Where do We Stand","volume":"99","author":"Sollini","year":"2018","journal-title":"Eur J Radiol"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.3390\/jcm9072156","article-title":"Radiomics Based on Thyroid Ultrasound Can Predict Distant Metastasis of Follicular Thyroid Carcinoma","volume":"9","author":"Kwon","year":"2020","journal-title":"J Clin Med"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1007\/s12020-021-02856-1","article-title":"Radiomics Analysis Improves (18)FDG PET\/CT-Based Risk Stratification of Cytologically Indeterminate Thyroid Nodules","volume":"75","author":"Giovanella","year":"2021","journal-title":"Endocrine"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.3760\/cma.j.issn.1005-1201.2020.03.003","article-title":"CT Radiomics Model for Differentiating Malignant and Benign Thyroid Nodules","volume":"54","author":"Kong","year":"2020","journal-title":"Chin J Radiol"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.13929\/j.issn.1672-8475.2021.02.010","article-title":"Radimics Based on Plain CT for Differential Diagnosis of \u22642cm Benign and Malignant Throid Nodules","volume":"18","author":"Hu","year":"2021","journal-title":"Chin J Intervent Imaging Ther"},{"key":"B13","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1038\/nrc1836","article-title":"Pathogenetic Mechanisms in Thyroid Follicular-Cell Neoplasia","volume":"6","author":"Kondo","year":"2006","journal-title":"Nat Rev Cancer"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.3969\/j.issn.1001-9324.2008.03.007","article-title":"CT Finding of Nodular Goitre Correlated With Pathologic Results","volume":"27","author":"Chen","year":"2008","journal-title":"J Clin Radiol"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2022.828259\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T01:21:13Z","timestamp":1651713673000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2022.828259\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,5]]},"references-count":14,"alternative-id":["10.3389\/fonc.2022.828259"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2022.828259","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,5]]},"article-number":"828259"}}